Iet Science Measurement & Technology最新文献

In this article, a new differential magnetic-field probe with high detection sensitivity for near-field scanning is proposed. The proposed probe consists of a U-shaped differential loop as a driven element, a pair of asymmetric inverted U-shaped differential loops as a parasitic element, two transmission line, two connected vias, two shorted vias, and two output ports. Connected and shorted vias are used to form the sensing structure with high sensitivity. Some shield vias are used to reject the unwanted electric–field coupling. The proposed probe is simulated, fabricated, and measured to demonstrate the design rationality. The measured results reveal that the presented probe has a higher detection sensitivity and a high electric-field suppression ratio greater than −35 dB in the range of 0.01 to 20 GHz.

This paper proposes a novel evaluation method to address the challenge of evaluating insulation degradation in power transformer windings based on incomplete online Internet of Things (IoT) sensing data. The method leverages the Wasserstein Slim Generative Adversarial Imputation Network with Gradient Penalty algorithm to fill the irregularly missing power transformer IoT perception data, including voltage, current, temperature, and partial discharge. Subsequently, electrical, thermal, and mechanical performance degradation damage indicators for transformer winding insulation are constructed using the filled and complete IoT perception data. By applying the tensor fusion algorithm, the characteristics of these degradation damage indicators are fused, leading to the development of a comprehensive degradation evaluation index for the winding insulation. The evaluation of the winding insulation degradation state is achieved through the minimum quantization error method. The proposed method is validated using the real-world transformer IoT perception data, and the experimental results demonstrate its ability to accurately assess the degree of winding insulation degradation, regardless of the presence of random or continuous irregularities in IoT sensing data.

The analysis of magnetic near-field radiation emissions (MNRE) has recently raised more attention in device-level electromagnetic compatibility testing. Pattern clustering of MNRE for integrated circuits manually is very time-consuming because of the multi-dimensional characteristics of MNRE, such as frequency, spatial position, emission intensity, etc. This paper proposes a novel pattern clustering method of MNRE, including strong emission frequency extraction, feature extraction, and density-based clustering. Ring oscillator and five working states are designed on a Field Programmable Gate Array with 256 Ball Grid Array package, which are used to create a complex multi-source emission case for verifying the effectiveness of the clustering method. The verification results show that the proposed method can correctly cluster the multi-source emission patterns. Further, the method is also applied to a Microcontroller Unit with unknown operating states; the results show that the proposed method also can effectively distinguish the unknown emission patterns and locate the unknown interference source. The accuracy of the interference source location is proven by 3D X-ray microscope inspection.

The mechanisms governing electric field and partial discharge (PD) under stress cone dislocation remain unclear. This study employs the finite element method to explore the relationship between the electric field, externally applied voltage, and length of dislocation. Subsequently, the correlation between voltage and electric field is determined. The relationship between voltage and PD is established through PD tests conducted under stress cone dislocation conditions. By considering the influence of voltage, the association between the electric field and PD is established, revealing the mechanism of PD initiation by electric field distortion in joints. The findings suggest that the lifetime and reliability were increased by wrapping semi-conductive self-adhesive tape around the exposed cross-linked polyethylene (XLPE) insulation layer. The electric field at a specific location is approximately proportional to the applied voltage. However, the slope is influenced by the composite insulation interface and the radial distance of the copper conductor. When crossing the interface between the XLPE insulation layer and the accessory insulation layer, the slope decreases significantly. The threshold for electric field PD is determined by examining the electric field distribution and the PD inception voltage during testing. When the voltage is sufficiently high, significant electric field distortions can occur at multiple points in the joint, potentially leading to concurrent PDs at several locations.

One crucial objective of this article is to present simplified and accurate approaches to study the current distribution and ground surface potential around the area surrounding an earthed and isolated system in case of a line-to-earth fault. The present study is done in the case of uniform and two-layer soils. Suggested models for calculating the distribution of the earth's surface current density, step, and touch voltages in grounded and isolated systems are presented, discussed, and adapted. The contact and arc resistances of line to ground faulty conductors are considered. Rods and/or grounding grids usually do grounded systems. The impact of both step and touch potentials and current density are investigated in this article. The methods of the calculations are based on the electrical concepts, the charge simulation method, and the imaging procedure for the grounding system. The suggested method can be considered a novel and simple technique for calculating the current distributions of the ground surface during line to ground fault. The results agree with those obtained by others, with the advantage of the proposed algorithm for its ease of application and simplicity. 3-D dimensions’ contours of the current density and the electric potential on the earth surface around the faulty point are presented in case of homogeneous and two layers’ soil. Finally, this article's novelty is to devise a method for calculating the step and touch potentials and current density around the point of contact of the conductor carrying the electric voltages in the event of faults occurring in the electrical network, whether the network is grounded or isolated. Such faults of electrical networks may cause human hazards.

This paper presents a novel bolt looseness detection method for power transmission towers based on vibration signal analysis. The proposed method utilizes pulse excitation to extract the vibration signal of the tower, which is then adaptively decomposed using the Variational Mode Decomposition of Spider Wasp optimizer (SWVMD). This overcomes limitations of traditional Variational Mode Decomposition methods by leveraging bio-inspired optimization to improve signal decomposition. Simulated signals processed with different optimization methods verify the superiority of the SWO approach. Field tests on a 110-kV transmission tower further demonstrate the effectiveness of the proposed SWVMD technique for analyzing on-site vibration data. A new improved intrinsic multiscale sample entropy feature is also introduced for bolt state characterization. A Spider Wasp Support Vector Machine classifier is developed to realize accurate bolt loosening monitoring using the extracted features. Dynamic response tests under varying bolt conditions show that the method can identify early loosening and reduce tower damage risks compared to conventional techniques. This novel vibration-based detection framework presents an innovative application of nature-inspired computing for power infrastructure health monitoring.

This paper proposes a deep learning method to predict the electromagnetic emission spectrum in the electromagnetic compatibility (EMC) test of aerospace products. A threshold-based data decomposition method is used to propose the spike signal, reconstruct the original test data, and solve the contradiction between the overfitting and prediction accuracy of the deep learning method to deal with the EMC test spectrum. Using a long short-term memory neural network architecture for predicting electromagnetic emission spectrum, the Bayesian optimization method is used to optimize the network hyperparameter, and the acquisition function is introduced into the loss function to improve the comprehensive training optimization of deep learning network. Apply the method to three numerical examples: signal line current conduction emission, power line voltage conduction emission, and electric field radiation emission. The analysis results indicate that at a 95% confidence level, the predicted electromagnetic emission spectrum is basically consistent with the test results. The prediction results can be used as the basis for EMC evaluation of aerospace electronic equipment.

With the rapid development of digital technologies such as intelligent control and information communication, digitization has gradually become an inevitable trend to promote technology innovation and industrial upgrading for the power grid. As the basis of digital technology, digital sensing technology will gradually penetrate all aspects of the power system for comprehensive perception and high operational reliability. First, this article introduces the application status and technical requirements of power intelligent sensor technology in various aspects of the power system, including the generation, transmission, transformation, distribution, and consumption of electricity. Next, domestic and foreign scholars' latest research achievements in power sensing technology are described from three major aspects, that is, optical fiber sensing technology, MEMS sensing technology, and sensor self-powered technology. Finally, future challenges for the electric power intelligent sensors are discussed and an outlook of future widespread application in the clean, safe, and efficient digital power grid is provided.

The pre-insertion resistors (PIR) within high-voltage circuit breakers are critical components and warm up by generating Joule heat when an electric current flows through them. Elevated temperature can lead to temporary closure failure and, in severe cases, the rupture of PIR. To accurately predict the temperature of PIR, this study combines finite element simulation techniques with Support Vector Regression (SVR) optimized by an Improved Whale Optimization Algorithm (IWOA) approach. The IWOA includes Tent mapping, a convergence factor based on the sigmoid function, and the Ornstein–Uhlenbeck variation strategy. The IWOA-SVR model is compared with the SSA-SVR and WOA-SVR. The results reveal that the prediction accuracies of the IWOA-SVR model were 90.2% and 81.5% (above 100°C) in the ± 3°C temperature deviation range and 96.3% and 93.4% (above 100°C) in the ± 4°C temperature deviation range, surpassing the performance of the comparative models. This research demonstrates that the method proposed can realize the online monitoring of the temperature of the PIR, which can effectively prevent thermal faults PIR and provide a basis for the opening and closing of the circuit breaker within a short period.

The leakage current characteristic monitoring of transmission line insulators is considered a worthy technique for insulator state prediction. In this paper, the harmonic analysis of leakage current signals has been performed to analyze the composite insulator condition. Experimental studies of asymmetrically aged, fully aged, and virgin insulators have been conducted. The effects of different aging types, pollution, and humidity on leakage current components, such as harmonics and the phase differences with the applied voltage, have been investigated. The analysis of the results shows that the effect of asymmetric aging on leakage current components is non-linear, and this non-linearity increases with the degree of pollution. Also, the type of insulator aging can be determined by the ratio of the third- and fifth-order leakage current harmonics. For instance, the classification of insulators into virgin, asymmetrically aged, and fully aged can be determined by examining the corresponding value ranges of 0–0.5, 0.5–0.9, and > 0.9, respectively. Similarly, the phase difference values of < 15% and those > 15% indicate the clean and polluted operating conditions individually.